Development of Deep-learning-based Technologies for Optical Coherence Tomography (OCT) and OCT-Angiography in Clinical Applications

Skin and oral cancers demand early detection to improve outcomes, but conventional biopsy is invasive, while non-invasive methods like dermoscopy struggle with resolution-depth trade-offs. Optical coherence tomography (OCT) and angiography (OCTA) offer non-invasive, high-resolution imaging (1-3 mm depth) for visualizing tissue microstructure and microvasculature without contrast agents, showing promise in dermatology and oral medicine. This thesis advances software and algorithms to optimize a lab-built swept-source OCT system, enhancing diagnostic accuracy and improving imaging speed through deep learning and image processing.
Key contributions address five areas: OCT-based segmentation and denoising, and OCTA-based reconstruction, super-resolution, and vasculature extraction. For OCT, lightweight networks improve skin tissue boundary segmentation, while a Swin transformer-based pipeline reduces speckle noise, enabling faster scans without quality loss. The Efficient Segmentation-Denoising Model concurrently denoises and segments oral tissues. For OCTA, deep-learning-based protocols reduce scan repetitions, shortening imaging time by >80%: the Image Reconstruction U-Net and U-shaped Fusion Convolutional Transformer generate high-quality angiograms from minimal two-repetition scan. Super-resolution transformers (Intraoral Micro-Angiography Super-Resolution Transformer and Angiography Reconstruction Transformer) enhance spatial resolution for sub-second OCTA imaging. Besides, the Vasculature Extraction Transformer directly extracts vascular signals from a single OCT scan, cutting acquisition time by 75% while preserving diagnostic quality.
These innovations address critical limitations in OCT/OCTA imaging, such as slow acquisition, motion artifacts, noise, and time-saving by automatic segmentation. By integrating deep learning, this study improves image quality, accelerates workflows, and enables precise, non-invasive assessment of lesion depth and vascular morphology. The advancements improve diagnostic workflow in skin cancer and oral pathologies, facilitating early detection and reducing reliance on invasive biopsies. Collectively, this research strengthens the clinical translation of OCT/OCTA, offering a patient-friendly paradigm for improving outcomes in dermatology and dentistry.